The Multi-source Probabilistic Inference group develops probabilistic machine learning models and inference techniques for analyzing and understanding complex heterogeneous data collections. For most data analysis tasks it is beneficial to jointly analyze all available data, but often the different data sources are not directly commensurable. For example, a data scientist studying demographics of a neighborhood might have static spatial information about the buildings, dynamic group-level information on public transportation, large collections of time-stamped and user-specific social media content both as text as images, and perhaps even some interview questionnaires. All of these sources provide information on the demographics, but standard modeling tools do not help much in providing an overall picture.

The goal of this research group is to overcome the theoretical and practical challenges needed for integrating such heterogeneous data sources, by building statistical models for various types of data and especially hierarchical models for joint analysis of them even in cases where there are no obvious ways of linking the sources with each other. We also develop statistical machine learning methods in more general, focusing on computationally efficient approximative inference, transfer learning, domain adaptation, and other techniques crucial for learning complex models (including deep neural networks) from limited data collections.

Open positions

There are currently no open calls, but we are constantly looking for talented postdoctoral researchers and PhD students to join the group. For both levels we consider both candidates with existing machine learning research track and those switching from other computational fields (physics, statistics, mathematics, economics, ...) towards machine learning and artificial intelligence. Contact the group leader by email if interested.